How do robots perform real-time decision-making?

Robots perform real-time decision-making by combining sensors, algorithms, and actuators to process data and act within milliseconds. Sensors like cameras, lidar, or inertial measurement units (IMUs) continuously gather environmental data, which is fed into a decision-making system. This system uses predefined logic, statistical models, or machine learning to interpret the data, predict outcomes, and select actions. For example, a self-driving car uses lidar scans to detect obstacles and decides whether to brake or steer based on the proximity and speed of nearby objects. The process must be fast enough to handle dynamic environments, requiring tight integration between hardware and software.

The algorithms used vary depending on the task. Simple systems might rely on rule-based logic, such as “if an obstacle is within 1 meter, stop.” More complex scenarios use probabilistic methods like Bayesian networks or machine learning models trained on large datasets. For instance, a drone navigating a forest might use a reinforcement learning model to adjust its flight path in real time, balancing obstacle avoidance with energy efficiency. These models often run on optimized hardware like GPUs or specialized chips (e.g., TPUs) to reduce latency. Middleware frameworks like ROS (Robot Operating System) help manage data flow between components, ensuring sensors, processors, and actuators communicate efficiently.

Real-time decision-making also involves trade-offs between accuracy and speed. For example, a robot vacuum might use a simplified version of SLAM (Simulation and Mapping) to prioritize quick navigation over highly detailed maps. Developers often test systems in simulations to validate performance under edge cases, like sudden sensor failures or unpredictable human behavior. Hardware limitations, such as battery life or processing power, further constrain choices. For instance, a warehouse robot might prioritize precomputed paths to conserve resources but switch to reactive algorithms when encountering unexpected obstacles. Balancing these factors requires iterative testing and optimization to ensure reliability across diverse scenarios.